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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 10, |
| 6 | + "metadata": {}, |
| 7 | + "outputs": [], |
| 8 | + "source": [ |
| 9 | + "#import Library\n", |
| 10 | + "import numpy as np\n", |
| 11 | + "import pandas as pd\n", |
| 12 | + "import ssl\n", |
| 13 | + "ssl._create_default_https_context = ssl._create_unverified_context\n", |
| 14 | + "from sklearn.model_selection import train_test_split\n", |
| 15 | + "from sklearn.preprocessing import StandardScaler\n", |
| 16 | + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n", |
| 17 | + "from sklearn.ensemble import RandomForestClassifier\n", |
| 18 | + "from sklearn.metrics import confusion_matrix\n", |
| 19 | + "from sklearn.metrics import accuracy_score" |
| 20 | + ] |
| 21 | + }, |
| 22 | + { |
| 23 | + "cell_type": "code", |
| 24 | + "execution_count": 11, |
| 25 | + "metadata": {}, |
| 26 | + "outputs": [], |
| 27 | + "source": [ |
| 28 | + "#import data using url\n", |
| 29 | + "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n", |
| 30 | + "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n", |
| 31 | + "dataset = pd.read_csv(url, names=names)" |
| 32 | + ] |
| 33 | + }, |
| 34 | + { |
| 35 | + "cell_type": "code", |
| 36 | + "execution_count": 12, |
| 37 | + "metadata": {}, |
| 38 | + "outputs": [], |
| 39 | + "source": [ |
| 40 | + "\n", |
| 41 | + "X = dataset.iloc[:, 0:4].values\n", |
| 42 | + "y = dataset.iloc[:, 4].values" |
| 43 | + ] |
| 44 | + }, |
| 45 | + { |
| 46 | + "cell_type": "code", |
| 47 | + "execution_count": 13, |
| 48 | + "metadata": {}, |
| 49 | + "outputs": [], |
| 50 | + "source": [ |
| 51 | + "#spilt data\n", |
| 52 | + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)" |
| 53 | + ] |
| 54 | + }, |
| 55 | + { |
| 56 | + "cell_type": "code", |
| 57 | + "execution_count": 14, |
| 58 | + "metadata": {}, |
| 59 | + "outputs": [], |
| 60 | + "source": [ |
| 61 | + "#scalling data\n", |
| 62 | + "sc = StandardScaler()\n", |
| 63 | + "X_train = sc.fit_transform(X_train)\n", |
| 64 | + "X_test = sc.transform(X_test)" |
| 65 | + ] |
| 66 | + }, |
| 67 | + { |
| 68 | + "cell_type": "code", |
| 69 | + "execution_count": 15, |
| 70 | + "metadata": {}, |
| 71 | + "outputs": [], |
| 72 | + "source": [ |
| 73 | + "#LDA Model\n", |
| 74 | + "lda = LDA(n_components=1)\n", |
| 75 | + "X_train = lda.fit_transform(X_train, y_train)\n", |
| 76 | + "X_test = lda.transform(X_test)" |
| 77 | + ] |
| 78 | + }, |
| 79 | + { |
| 80 | + "cell_type": "code", |
| 81 | + "execution_count": 16, |
| 82 | + "metadata": {}, |
| 83 | + "outputs": [], |
| 84 | + "source": [ |
| 85 | + "#rendom Forest classifier\n", |
| 86 | + "classifier = RandomForestClassifier(max_depth=2, random_state=0)\n", |
| 87 | + "\n", |
| 88 | + "classifier.fit(X_train, y_train)\n", |
| 89 | + "y_pred = classifier.predict(X_test)" |
| 90 | + ] |
| 91 | + }, |
| 92 | + { |
| 93 | + "cell_type": "code", |
| 94 | + "execution_count": 17, |
| 95 | + "metadata": {}, |
| 96 | + "outputs": [ |
| 97 | + { |
| 98 | + "name": "stdout", |
| 99 | + "output_type": "stream", |
| 100 | + "text": [ |
| 101 | + "[[11 0 0]\n", |
| 102 | + " [ 0 13 0]\n", |
| 103 | + " [ 0 0 6]]\n", |
| 104 | + "Accuracy1.0\n" |
| 105 | + ] |
| 106 | + } |
| 107 | + ], |
| 108 | + "source": [ |
| 109 | + "#confusion matrix\n", |
| 110 | + "cm = confusion_matrix(y_test, y_pred)\n", |
| 111 | + "print(cm)\n", |
| 112 | + "print('Accuracy' + str(accuracy_score(y_test, y_pred)))" |
| 113 | + ] |
| 114 | + } |
| 115 | + ], |
| 116 | + "metadata": { |
| 117 | + "kernelspec": { |
| 118 | + "display_name": "Python 3", |
| 119 | + "language": "python", |
| 120 | + "name": "python3" |
| 121 | + }, |
| 122 | + "language_info": { |
| 123 | + "codemirror_mode": { |
| 124 | + "name": "ipython", |
| 125 | + "version": 3 |
| 126 | + }, |
| 127 | + "file_extension": ".py", |
| 128 | + "mimetype": "text/x-python", |
| 129 | + "name": "python", |
| 130 | + "nbconvert_exporter": "python", |
| 131 | + "pygments_lexer": "ipython3", |
| 132 | + "version": "3.7.7" |
| 133 | + } |
| 134 | + }, |
| 135 | + "nbformat": 4, |
| 136 | + "nbformat_minor": 4 |
| 137 | +} |
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